input_layer = tf.keras.layers.Input(shape=inputs.shape[1:])是什么意思
时间: 2024-04-29 19:20:21 浏览: 13
`tf.keras.layers.Input`是一个Keras层,用于接收模型的输入数据。这里,我们使用`tf.keras.layers.Input`来构建输入层,其中`shape`参数表示输入数据的形状。
`inputs.shape[1:]`表示输入数据的形状,其中`inputs`是输入数据的张量。`inputs.shape`是一个元组,包含了输入数据的形状信息,如`(batch_size, input_dim)`。`inputs.shape[1:]`则表示除了`batch_size`之外的其它维度,也就是`input_dim`。
因此,`tf.keras.layers.Input(shape=inputs.shape[1:])`的意思是:创建一个输入层,其输入数据的形状与`inputs`张量的形状相同,但去掉了`batch_size`这一维。这样做的好处是,可以在训练时灵活地调整`batch_size`的大小,而不必每次都修改模型的输入层。
相关问题
下面代码在tensorflow中出现了init() missing 1 required positional argument: 'cell'报错: class Model(): def init(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out) def build_discriminator(self): def d_layer(layer_input, filters, f_size=4, bn=True): d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) if bn: d = tf.keras.layers.BatchNormalization(momentum=0.8)(d) d = tf.keras.layers.LeakyReLU(alpha=0.2)(d) return d img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3)) img_B = tf.keras.layers.Input(shape=(128, 128, 3)) df = 32 lstm_out = ConvRNN2D(filters=df, kernel_size=4, padding="same")(img_A) lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out) combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B]) d1 = d_layer(combined_imgs, df)#64 d2 = d_layer(d1, df * 2)#32 d3 = d_layer(d2, df * 4)#16 d4 = d_layer(d3, df * 8)#8 validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4) return tf.keras.Model([img_A, img_B], validity)
你在定义 Model 类时,没有传入参数 cell,但是在代码中使用了 ConvRNN2D 的实例化对象,这个对象需要一个 cell 参数。你需要在初始化函数中添加这个参数,如下所示:
```
class Model():
def __init__(self, cell):
self.img_seq_shape=(10,128,128,3)
self.img_shape=(128,128,3)
self.train_img=dataset
patch = int(128 / 2 ** 4)
self.disc_patch = (patch, patch, 1)
self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)
self.build_generator=self.build_generator()
self.build_discriminator=self.build_discriminator(cell)
self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy'])
self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer)
img_seq_A = Input(shape=(10,128,128,3))
img_B = Input(shape=self.img_shape)
fake_B = self.build_generator(img_seq_A)
self.build_discriminator.trainable = False
valid = self.build_discriminator([img_seq_A, fake_B])
self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B])
self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy'])
def build_generator(self):
def res_net(inputs, filters):
x = inputs
net = conv2d(x, filters // 2, (1, 1), 1)
net = conv2d(net, filters, (3, 3), 1)
net = net + x
return net
def conv2d(inputs, filters, kernel_size, strides):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
return x
d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
out= ConvRNN2D(cell, filters=32, kernel_size=3,padding='same')(d0)
out=tf.keras.layers.Conv2D(3,1,1,'same')(out)
return keras.Model(inputs=d0, outputs=out)
def build_discriminator(self, cell):
def d_layer(layer_input, filters, f_size=4, bn=True):
d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
if bn:
d = tf.keras.layers.BatchNormalization(momentum=0.8)(d)
d = tf.keras.layers.LeakyReLU(alpha=0.2)(d)
return d
img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3))
img_B = tf.keras.layers.Input(shape=(128, 128, 3))
df = 32
lstm_out = ConvRNN2D(cell, filters=df, kernel_size=4, padding="same")(img_A)
lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out)
combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B])
d1 = d_layer(combined_imgs, df)#64
d2 = d_layer(d1, df * 2)#32
d3 = d_layer(d2, df * 4)#16
d4 = d_layer(d3, df * 8)#8
validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return tf.keras.Model([img_A, img_B], validity)
```
import tensorflow as tf import tensorflow_hub as hub from tensorflow.keras import layers import bert import numpy as np from transformers import BertTokenizer, BertModel # 设置BERT模型的路径和参数 bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\bert1.ckpt" max_seq_length = 128 train_batch_size = 32 learning_rate = 2e-5 num_train_epochs = 3 # 加载BERT模型 def create_model(): input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_word_ids") input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask") segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids") bert_layer = hub.KerasLayer(bert_path, trainable=True) pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids]) output = layers.Dense(1, activation='sigmoid')(pooled_output) model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=output) return model # 准备数据 def create_input_data(sentences, labels): tokenizer = bert.tokenization.FullTokenizer(vocab_file=bert_path + "trainer/vocab.small", do_lower_case=True) # tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') input_ids = [] input_masks = [] segment_ids = [] for sentence in sentences: tokens = tokenizer.tokenize(sentence) tokens = ["[CLS]"] + tokens + ["[SEP]"] input_id = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_id) segment_id = [0] * len(input_id) padding_length = max_seq_length - len(input_id) input_id += [0] * padding_length input_mask += [0] * padding_length segment_id += [0] * padding_length input_ids.append(input_id) input_masks.append(input_mask) segment_ids.append(segment_id) return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels) # 加载训练数据 train_sentences = ["Example sentence 1", "Example sentence 2", ...] train_labels = [0, 1, ...] train_input_ids, train_input_masks, train_segment_ids, train_labels = create_input_data(train_sentences, train_labels) # 构建模型 model = create_model() model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy']) # 开始微调 model.fit([train_input_ids, train_input_masks, train_segment_ids], train_labels, batch_size=train_batch_size, epochs=num_train_epochs)这段代码有什么问题吗?
这段代码存在一些问题:
1. `bert_path` 的值不应该是 ".ckpt" 结尾的文件路径,而是BERT模型的目录路径,例如:`bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\"`。
2. `vocab_file` 参数应该是BERT模型目录下的 "vocab.txt" 文件路径,而不是训练器目录下的 "vocab.small" 文件路径。例如:`tokenizer = bert.tokenization.FullTokenizer(vocab_file=bert_path + "vocab.txt", do_lower_case=True)`。
3. `create_input_data` 函数中的 `tokenizer` 对象应该是从 `BertTokenizer` 类中导入的。因此,需要将 `from transformers import BertTokenizer` 加入代码开头,并使用 `BertTokenizer.from_pretrained()` 函数来加载BERT的tokenizer。
4. `train_sentences` 和 `train_labels` 应该是训练数据集,但是在代码中缺失了这些数据的定义和加载。
5. `create_input_data` 函数返回的 `labels` 应该是一维的,而不是二维的。因此,需要修改函数 `return` 语句为:`return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1)`。
6. 在 `model.fit` 中,需要指定验证集的参数 `validation_data`,以便在训练过程中评估模型的性能。
综上所述,以下是修改后的代码示例:
```
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import bert
import numpy as np
from transformers import BertTokenizer, BertModel
# 设置BERT模型的路径和参数
bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\"
max_seq_length = 128
train_batch_size = 32
learning_rate = 2e-5
num_train_epochs = 3
# 加载BERT模型
def create_model():
input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_word_ids")
input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids")
bert_layer = hub.KerasLayer(bert_path, trainable=True)
pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])
output = layers.Dense(1, activation='sigmoid')(pooled_output)
model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=output)
return model
# 准备数据
def create_input_data(sentences, labels):
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_ids = []
input_masks = []
segment_ids = []
for sentence in sentences:
tokens = tokenizer.tokenize(sentence)
tokens = ["[CLS]"] + tokens + ["[SEP]"]
input_id = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_id)
segment_id = [0] * len(input_id)
padding_length = max_seq_length - len(input_id)
input_id += [0] * padding_length
input_mask += [0] * padding_length
segment_id += [0] * padding_length
input_ids.append(input_id)
input_masks.append(input_mask)
segment_ids.append(segment_id)
return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1)
# 加载训练数据
train_sentences = ["Example sentence 1", "Example sentence 2", ...]
train_labels = [0, 1, ...]
train_input_ids, train_input_masks, train_segment_ids, train_labels = create_input_data(train_sentences, train_labels)
# 构建模型
model = create_model()
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss='binary_crossentropy',
metrics=['accuracy'])
# 开始微调
model.fit([train_input_ids, train_input_masks, train_segment_ids], train_labels,
batch_size=train_batch_size,
epochs=num_train_epochs,
validation_data=([val_input_ids, val_input_masks, val_segment_ids], val_labels))
```